EpiLearn: A Python Library for Machine Learning in Epidemic Modeling
This is an incremental tool for epidemiologists and data scientists to bridge the gap between traditional epidemic modeling and machine learning methods.
The authors tackled the gap between existing epidemic modeling packages and modern machine learning by developing EpiLearn, a Python toolkit that provides a unified framework for training and evaluating models on forecasting and source detection tasks, along with simulation, visualization, and an interactive web application.
EpiLearn is a Python toolkit developed for modeling, simulating, and analyzing epidemic data. Although there exist several packages that also deal with epidemic modeling, they are often restricted to mechanistic models or traditional statistical tools. As machine learning continues to shape the world, the gap between these packages and the latest models has become larger. To bridge the gap and inspire innovative research in epidemic modeling, EpiLearn not only provides support for evaluating epidemic models based on machine learning, but also incorporates comprehensive tools for analyzing epidemic data, such as simulation, visualization, transformations, etc. For the convenience of both epidemiologists and data scientists, we provide a unified framework for training and evaluation of epidemic models on two tasks: Forecasting and Source Detection. To facilitate the development of new models, EpiLearn follows a modular design, making it flexible and easy to use. In addition, an interactive web application is also developed to visualize the real-world or simulated epidemic data. Our package is available at https://github.com/Emory-Melody/EpiLearn.